Anders Malthe‐Sørenssen

This paper develops a deep learning model to predict stem cell efficiency for beta cell differentiation using phase-contrast images, achieving high accuracy in clone classification.

This paper investigates grid cells as a conformal isometric map of space, showing that a module of at least seven grid cells can achieve a distance-preserving representation with regular hexagonal phase patterns.

This study shows that MEC and POR encode task- and behavior-related variables during non-spatial learning, with MEC showing stronger within-cluster correlations and anatomical organization.

The paper shows that reactivations in the lateral visual cortex are essential for visual association learning consolidation, with inhibitory neuron manipulation reducing cue-specific reactivations and learning.

This paper shows that grid-like spatial representations can emerge in neural networks without path integration, suggesting distance preservation and capacity constraints may be key factors.

This paper develops a neural network model that learns place cell-like spatial representations and remapping behaviors, showing clustering patterns similar to biological place cells in mice.

The paper shows that toroidal cells, not grid cells, are crucial for path integration in virtual agents using RNN models.

The paper introduces a 3-D Kirchhoff-Nernst-Planck framework to model electrodiffusive effects around neurons, demonstrating its efficiency for large-scale simulations.

The paper introduces Exdir, an open standard for organizing data storage in experimental pipelines as an alternative to HDF5, improving data sharing and reproducibility.